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      <div class="title">prtClusterGmm/prtClusterGmm</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtClusterGmm</span>   Gaussian mixture model clustering object
 
     CLUSTER = <span class="helptopic">prtClusterGmm</span> returns a GMM clustering object.
 
     CLUSTER = <span class="helptopic">prtClusterGmm</span>(PROPERTY1, VALUE1, ...) constructs a
     prtClassFld object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
 
     A <span class="helptopic">prtClusterGmm</span> object inherits all properties from the abstract
     class prtCluster. In addition is has the following properties:
 
     nClusters          - Number of cluster centers to learn 
 
     A <span class="helptopic">prtClusterGmm</span> clustering algorithm trains a prtRvGmm random
     variable on training data, and at run time, the clustering
     algorithm outputs the posterior probability of any particular
     point being drawn from one of the nClusters Guassian components.
 
     A <span class="helptopic">prtClusterGmm</span> object inherits the TRAIN, RUN, CROSSVALIDATE and
     KFOLDS methods from prtAction. It also inherits the PLOT method from
     prtCluster.
 
    Example:
 
    ds = prtDataGenUnimodal         % Load a data set
    clusterAlgo = <span class="helptopic">prtClusterGmm</span>;    % Create a clustering object
    clusterAlgo.nClusters = 2;      % Set the number of clusters
 
    % Set the internal decision rule to be MAP. Not required for
    % clustering, but necessary to plot the results.
    clusterAlgo.internalDecider = prtDecisionMap;
  
    clusterAlgo = clusterAlgo.train(ds);  % Train
    plot(clusterAlgo);                    % Plot the trained object</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See Also</div><div class="footerlink"> <a href="./../prtCluster.html">prtCluster</a>, <a href="./../prtClusterKmeans.html">prtClusterKmeans</a>
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